# Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder

arXiv: https://arxiv.org/abs/2006.08101 · HTML: https://mlsanity.com/paper/2006.08101/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval](https://mlsanity.com/paper/2203.06169/) (2022)
- [Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders](https://mlsanity.com/paper/2109.07169/) (2021)
- [Cross-Layer Discrete Concept Discovery for Interpreting Language Models](https://mlsanity.com/paper/2506.20040/) (2025)
- [Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery](https://mlsanity.com/paper/2602.02726/) (2026)
- [Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables](https://mlsanity.com/paper/2211.03616/) (2022)
- [The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited](https://mlsanity.com/paper/2407.19532/) (2024)

## Abstract (arXiv)

Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs. Existing works usually ignore the context that is not explicitly provided, resulting in a context-independent semantic representation that struggles to support the generation. To address this, we propose an approach that automatically finds evidence for an event from a large text corpus, and leverages the evidence to guide the generation of inferential texts. Our approach works in an encoder-decoder manner and is equipped with a Vector Quantised-Variational Autoencoder, where the encoder outputs representations from a distribution over discrete variables. Such discrete representations enable automatically selecting relevant evidence, which not only facilitates evidence-aware generation, but also provides a natural way to uncover rationales behind the generation. Our approach provides state-of-the-art performance on both Event2Mind and ATOMIC datasets. More importantly, we find that with discrete representations, our model selectively uses evidence to generate different inferential texts.